{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.Chem import rdMolDescriptors\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions\n",
    "from sascorer import calculateScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File b'generated' does not exist",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-175-baf82daca166>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtraining_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_dataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smiles'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtraining_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smiles'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mgenerated_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerated_dataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smiles'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mgenerated_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerated_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smiles'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/bowen/anaconda3/envs/oepython_feb_22_2018/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    653\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/bowen/anaconda3/envs/oepython_feb_22_2018/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 405\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/bowen/anaconda3/envs/oepython_feb_22_2018/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/bowen/anaconda3/envs/oepython_feb_22_2018/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    983\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 985\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    986\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/bowen/anaconda3/envs/oepython_feb_22_2018/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1605\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__ (pandas/_libs/parsers.c:4209)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source (pandas/_libs/parsers.c:8873)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: File b'generated' does not exist"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "## file io\n",
    "training_dataset_path = '250k_rndm_zinc_drugs_clean.smi'  # single smiles column\n",
    "generated_dataset_path = 'generated'  # single smiles column\n",
    "\n",
    "training_df = pd.read_csv(training_dataset_path, header=None, names=['smiles'])\n",
    "training_s = training_df['smiles']\n",
    "generated_df = pd.read_csv(generated_dataset_path, header=None, names=['smiles'])\n",
    "generated_s = generated_df['smiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # TEST datasets, for DEBUG only\n",
    "# training_s = training_df['smiles'][100000:200000]\n",
    "# generated_s = training_df['smiles'][10000:20000].append(training_df['smiles'][0:5]).append(training_df['smiles'][10:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take a sample from the training dataset and the generated dataset\n",
    "n = 5000\n",
    "assert n <= len(training_s)\n",
    "assert n <= len(generated_s)\n",
    "training_s = training_s.sample(n)\n",
    "generated_s = generated_s.sample(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% unique within generated: 1.0\n",
      "% unique within training: 1.0\n",
      "% unique compared to training: 1.0\n",
      "train MW mean: 331.7538650000001\n",
      "train MW std: 62.267483388982264\n",
      "train logP mean: 2.4558190400000015\n",
      "train logP std: 1.4353859300011687\n",
      "train SA mean: 3.047639318291274\n",
      "train SA std: 0.8380007680366827\n",
      "train QED mean: 0.7307172288918757\n",
      "train QED std: 0.1410523905972421\n",
      "gen MW mean: 332.06193800000005\n",
      "gen MW std: 61.93938867358769\n",
      "gen logP mean: 2.467269332000001\n",
      "gen logP std: 1.4333459196904967\n",
      "gen SA mean: 3.034395608129303\n",
      "gen SA std: 0.833384742603282\n",
      "gen QED mean: 0.7340933326469314\n",
      "gen QED std: 0.13719133606531775\n"
     ]
    }
   ],
   "source": [
    "## statistics\n",
    "\n",
    "# % unique within generated\n",
    "print('% unique within generated: {}'.format(len(generated_s.unique()) / len(generated_s)))\n",
    "\n",
    "# % unique within training (as a check)\n",
    "print('% unique within training: {}'.format(len(training_s.unique()) / len(training_s)))\n",
    "\n",
    "# % unique compared to training\n",
    "gen_set = set(generated_s)\n",
    "training_set = set(training_s)\n",
    "num_intersect = len(gen_set.intersection(training_set))\n",
    "print('% unique compared to training: {}'.format((len(gen_set) - num_intersect) / len(gen_set)))\n",
    "\n",
    "# internal diversity\n",
    "# TODO(Bowen): figure out a reasonable way to calculate. Look at https://github.com/gablg1/ORGAN and https://github.com/mostafachatillon/ChemGAN-challenge\n",
    "\n",
    "## training dataset\n",
    "# MW\n",
    "mw_train_list = [Descriptors.MolWt(Chem.MolFromSmiles(s)) for s in training_s]\n",
    "print('train MW mean: {}'.format(np.array(mw_train_list).mean()))\n",
    "print('train MW std: {}'.format(np.array(mw_train_list).std()))\n",
    "# logP\n",
    "logp_train_list = [Descriptors.MolLogP(Chem.MolFromSmiles(s)) for s in training_s]\n",
    "print('train logP mean: {}'.format(np.array(logp_train_list).mean()))\n",
    "print('train logP std: {}'.format(np.array(logp_train_list).std()))\n",
    "# SA\n",
    "sa_train_list = [calculateScore(Chem.MolFromSmiles(s)) for s in training_s]\n",
    "print('train SA mean: {}'.format(np.array(sa_train_list).mean()))\n",
    "print('train SA std: {}'.format(np.array(sa_train_list).std()))\n",
    "# QED\n",
    "qed_train_list = [Descriptors.qed(Chem.MolFromSmiles(s)) for s in training_s]\n",
    "print('train QED mean: {}'.format(np.array(qed_train_list).mean()))\n",
    "print('train QED std: {}'.format(np.array(qed_train_list).std()))\n",
    "\n",
    "## generated dataset\n",
    "# MW\n",
    "mw_gen_list = [Descriptors.MolWt(Chem.MolFromSmiles(s)) for s in generated_s]\n",
    "print('gen MW mean: {}'.format(np.array(mw_gen_list).mean()))\n",
    "print('gen MW std: {}'.format(np.array(mw_gen_list).std()))\n",
    "# logP\n",
    "logp_gen_list = [Descriptors.MolLogP(Chem.MolFromSmiles(s)) for s in generated_s]\n",
    "print('gen logP mean: {}'.format(np.array(logp_gen_list).mean()))\n",
    "print('gen logP std: {}'.format(np.array(logp_gen_list).std()))\n",
    "# SA\n",
    "sa_gen_list = [calculateScore(Chem.MolFromSmiles(s)) for s in generated_s]\n",
    "print('gen SA mean: {}'.format(np.array(sa_gen_list).mean()))\n",
    "print('gen SA std: {}'.format(np.array(sa_gen_list).std()))\n",
    "# QED\n",
    "qed_gen_list = [Descriptors.qed(Chem.MolFromSmiles(s)) for s in generated_s]\n",
    "print('gen QED mean: {}'.format(np.array(qed_gen_list).mean()))\n",
    "print('gen QED std: {}'.format(np.array(qed_gen_list).std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ss8/PjHbMn/DpuLZ0kYtxJHavXiFEn6ZuP8++f5pAWOUfb8hhRoYZADXQS/hn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\nPZp62vNlNO5bMtIWbcPrpe+2D6m1u1mUNhUDXi4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26807e19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jRKfyJrbJVN7MtsERyPZggIyTNWy94hOkJduZWZyL7VTNOeQ8//vZOcz5yS5a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\nQggRYyQ5CyGEEDFGkrMQQggRY/4f5SQmVA0eGOcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26762ce978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vksiB99FrXZAQIqP1GcKTJ0/m+eef57LLLqOmpgaXy4XVagVg1qxZzJo1C4CW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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2686cab198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tf28jh+3p7LVnM8ocZYzTgqJYhuz17XbHkL3W2SQnmbho3DSe/GsFBzo0bh7m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\nBeLw9asApGshptt83GLz41ANE9MLIaKdmpjM3xc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2680096dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms\n",
    "plt.style.use('ggplot')\n",
    "n_bins = 50\n",
    "\n",
    "# MW\n",
    "sns.distplot(mw_train_list, hist=True, kde=True, label='train MW')\n",
    "sns.distplot(mw_gen_list, hist=True, kde=True, label='gen MW')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('MW')\n",
    "plt.show()\n",
    "# logP\n",
    "sns.distplot(logp_train_list, hist=True, kde=True, label='train logP')\n",
    "sns.distplot(logp_gen_list, hist=True, kde=True, label='gen logP')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('logP')\n",
    "plt.show()\n",
    "# SA\n",
    "sns.distplot(sa_train_list, hist=True, kde=True, label='train SA')\n",
    "sns.distplot(sa_gen_list, hist=True, kde=True, label='gen SA')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('SA')\n",
    "plt.show()\n",
    "# QED\n",
    "sns.distplot(qed_train_list, hist=True, kde=True, label='train QED')\n",
    "sns.distplot(qed_gen_list, hist=True, kde=True, label='gen QED')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('QED')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  reward specific statistics\n",
    "\n",
    "# ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# FCD score\n",
    "\n",
    "#TODO(Bowen)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
